First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
DATA SCIENCE
Course unit
KNOWLEDGE AND DATA MINING
SCP7079318, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2018/19

Information on the course unit
Degree course Second cycle degree in
DATA SCIENCE
SC2377, Degree course structure A.Y. 2017/18, A.Y. 2018/19
N0
bring this page
with you
Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination KNOWLEDGE AND DATA MINING
Website of the academic structure http://datascience.scienze.unipd.it/2018/laurea_magistrale
Department of reference Department of Mathematics
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge LUCIANO SERAFINI 000000000000

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses INF/01 Computer Science 3.0
Core courses ING-INF/05 Data Processing Systems 3.0

Course unit organization
Period Second semester
Year 1st Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Examination board not defined

Syllabus
Prerequisites: Suggested basic knowledge of logics and statistics.
Target skills and knowledge: Introduce the students to the principles for logics for knowledge representation and reasoning, statistical relational learning, and the combination of the two in order to build system for learning and reasoning in hybrid domains.
Examination methods: Final examination based on: written examination or project development.
Assessment criteria: Critical knowledge of the course topics. Ability to present and apply the studied material
Course unit contents: (A) Logics for knowledge representation:
(A.i) introduction to propositional logics, syntax, semantics, decision procedure. Satisfiability, weighted satisfiability, and best satisfiability.
(A.ii) First order logics, syntax, semantics, resolution and unification.
(A.iii) Fuzzy logics, syntax, semantics, and reasoning.

(B) statistical relational learning:
(B.i) Graphical models
(B,ii) Markov Logic Networks
(B.iii) Probabilistic prolog,
(B.iii) Logic Tensor Networks
Planned learning activities and teaching methods: Lectures supported by exercises and lab
Additional notes about suggested reading: Lecture notes and slides for the part not covered by textbooks will be provided.
Textbooks (and optional supplementary readings)